Bibliographic record
Abstract
Traditionally, it has been thought that, assuming other conditions are satisfied, your action must be morally worthy or good if you are acting solely from good motives. There is a lively dispute as to which motives are good, but whichever motives are good, acting solely from good motives is not always good and can even be bad on the whole. We may act rightly from a good motive while being indifferent to what matters most. Indifference, I argue, can make our actions less than ideally good and at times even bad. Traditional theories, however, cannot accommodate cases of indifference by assuming absent and ineffective motives can never make a difference to an action’s moral value. Absent as well as ineffective motives can make an action less good and at times even bad. To accommodate this, we need to adopt a proportionality principle in assessing an action. An action is made good to a degree in proportion to the goodness of its effective motives. But an action is also made bad to a degree in proportion to the disproportion it exhibits through its whole set of relevant motives including not only effective motives but also absent as well as ineffective ones.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.039 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.007 | 0.053 |
| Scholarly communication | 0.008 | 0.016 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".